arXiv 11 Aug 2020 · Econometrics · publishedEconomics Letters (2021) · 2 citations (OpenAlex)
arXiv:2008.04708 · PDF · DOI · OpenAlex · Extracted main text
We study kmeans clustering estimation of panel data models with a latent group structure and $N$ units and $T$ time periods under long panel asymptotics. We show that the group-specific coefficients can be estimated at the parametric root $NT$ rate even if error variances diverge as $T \to \infty$ and some units are asymptotically misclassified. This limit case approximates empirically relevant settings and is not covered by existing asymptotic results.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Bonhomme, Elena (2015) Grouped patterns of heterogeneity in panel data | 0.909 | 8 | 4 | 75% |
| 2 | Su, Liangjun, Shi, Zhentao, Phillips, Peter (2016) Identifying latent structures in panel data | 0.511 | 2 | 1 | 100% |
| 3 | Bonhomme, Thibaut, Manresa, Elena (2019) Discretizing unobserved heterogeneity | 0.405 | 1 | 1 | 100% |
| 4 | Okui, Ryo, Wang, Wendun (2020) Heterogeneous structural breaks in panel data models self | 0.405 | 1 | 1 | 100% |
| 5 | Pollard, David (1981) Strong consistency of k-means clustering | 0.405 | 1 | 1 | 100% |
| 6 | Pollard, David (1982) A central limit theorem for $ k $-means clustering | 0.405 | 1 | 1 | 100% |
| 7 | Vogt, Michael, Linton, Oliver (2017) Classification of nonparametric regression functions in heterogeneous panels | 0.405 | 1 | 1 | 100% |
| 8 | Wang, Wuyi, Phillips, Peter C. B., Su, Liangjun (2018) Homogeneity pursuit in panel data models: theory and applications | 0.405 | 1 | 1 | 100% |
| 9 | Kuchibhotla, Arun Kumar, Chakrabortty, Abhishek (2018) Moving beyond sub-gaussianity in high-dimensional statistics: Applications in covariance estimation and linear regression | 0.000 | 1 | 1 | 0% |
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